[source] # KNN Imputer An unsupervised imputer that replaces missing values in a dataset with the distance-weighted average of the samples' *k* nearest neighbors' values. The average for a continuous feature column is defined as the mean of the values of each donor. Similarly, average is defined as the *most frequent* value for categorical features. !!! note Requires a NaN safe distance kernel such as [Safe Euclidean](../kernels/distance/safe-euclidean.md) for continuous features. **Interfaces:** [Transformer](api.md#transformers), [Stateful](api.md#stateful), [Persistable](../persistable.md) **Data Type Compatibility:** Depends on distance kernel ## Parameters | # | Name | Default | Type | Description | |---|---|---|---|---| | 1 | k | 5 | int | The number of nearest neighbor donors to consider when imputing a value. | | 2 | weighted | false | bool | Should we use distances as weights when selecting a donor sample? | | 3 | categoricalPlaceholder | '?' | string | The categorical placeholder denoting the category that contains missing values. | | 4 | tree | BallTree | Spatial | The spatial tree used to run nearest neighbor searches. | ## Example ```php use Rubix\ML\Transformers\KNNImputer; use Rubix\ML\Graph\Trees\BallTee; use Rubix\ML\Kernels\Distance\SafeEuclidean; $transformer = new KNNImputer(10, false, '?', new BallTree(30, new SafeEuclidean())); ``` ## Additional Methods This transformer does not have any additional methods. ## References [^1]: O. Troyanskaya et al. (2001). Missing value estimation methods for DNA microarrays.